%0 Conference Paper %1 WebbCummingRichardsYum90 %A Webb, G. I. %A Cumming, G. %A Richards, T. %A Yum, K-K. %B Proceedings of the IFIP TC3 International Conference on Advanced Research on Computers in Education (ARCE'90) %C Amsterdam %D 1990 %E Lewis, R. %E Otsuki, S. %I Elsevier %K Based Computer Feature Learning Modeling, %P 101-108 %T Educational Evaluation of Feature Based Modelling in a Problem Solving Domain %X Feature-Based Modelling is a machine learning based cognitive modelling methodology. An intelligent educational system has been implemented, for the purpose of evaluating the methodology, which helps students learn about the unification of terms from the Prolog programming language. The system has been used by Third Year Computer Science students at La Trobe University during September 1989. Students were randomly allocated to an Experimental condition, in which FBM modelling was used to select tasks, and give extra comments, or to a Control condition in which similar tasks and comments were given, but without FBM tailoring to the individual. Ratings of task appropriateness, and comment usefulness, were collected on-line as the students worked with the tutor; overall ratings were obtained by questionnaire at the end; and semester exam results were examined. Despite the fact that only a minority of students showed sufficient misunderstanding for FBM to have potential value, of the ten comparisons chat relate most directly to the aims of the Tutor, while in no case reaching significance, seven were in favour of the Tutor, and only two against. These preliminary results are very encouraging for the FBM principles of the Tutor.

@inproceedings{WebbCummingRichardsYum90, abstract = {Feature-Based Modelling is a machine learning based cognitive modelling methodology. An intelligent educational system has been implemented, for the purpose of evaluating the methodology, which helps students learn about the unification of terms from the Prolog programming language. The system has been used by Third Year Computer Science students at La Trobe University during September 1989. Students were randomly allocated to an Experimental condition, in which FBM modelling was used to select tasks, and give extra comments, or to a Control condition in which similar tasks and comments were given, but without FBM tailoring to the individual. Ratings of task appropriateness, and comment usefulness, were collected on-line as the students worked with the tutor; overall ratings were obtained by questionnaire at the end; and semester exam results were examined. Despite the fact that only a minority of students showed sufficient misunderstanding for FBM to have potential value, of the ten comparisons chat relate most directly to the aims of the Tutor, while in no case reaching significance, seven were in favour of the Tutor, and only two against. These preliminary results are very encouraging for the FBM principles of the Tutor. }, added-at = {2016-03-20T05:42:04.000+0100}, address = {Amsterdam}, audit-trail = {Pre-pub pdf posted 26/5/05}, author = {Webb, G. I. and Cumming, G. and Richards, T. and Yum, K-K.}, biburl = {https://www.bibsonomy.org/bibtex/2f5d5b95a37193a57b017e666ee7902a8/giwebb}, booktitle = {Proceedings of the IFIP TC3 International Conference on Advanced Research on Computers in Education (ARCE'90)}, editor = {Lewis, R. and Otsuki, S.}, interhash = {235648257a032c2b2a323506a9d59212}, intrahash = {f5d5b95a37193a57b017e666ee7902a8}, keywords = {Based Computer Feature Learning Modeling,}, location = {Tokyo, Japan}, pages = {101-108}, publisher = {Elsevier}, timestamp = {2016-03-20T05:42:04.000+0100}, title = {Educational Evaluation of Feature Based Modelling in a Problem Solving Domain}, year = 1990 }